|
--- |
|
license: mit |
|
base_model: microsoft/layoutlm-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- layoutlmv3 |
|
model-index: |
|
- name: LayoutLM_Invoice6 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# LayoutLM_Invoice6 |
|
|
|
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv3 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0219 |
|
- Ax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} |
|
- Endor Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} |
|
- Nvoice Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} |
|
- Otal Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} |
|
- Ustomer Address: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} |
|
- Ustomer Name: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} |
|
- Overall Precision: 0.9846 |
|
- Overall Recall: 0.9697 |
|
- Overall F1: 0.9771 |
|
- Overall Accuracy: 0.9939 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 6 |
|
- eval_batch_size: 3 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- training_steps: 300 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Ax Amount | Endor Name | Nvoice Number | Otal Amount | Ustomer Address | Ustomer Name | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
|
| 0.8763 | 6.25 | 50 | 0.2290 | {'precision': 1.0, 'recall': 0.5454545454545454, 'f1': 0.7058823529411764, 'number': 11} | {'precision': 0.8181818181818182, 'recall': 0.8181818181818182, 'f1': 0.8181818181818182, 'number': 11} | {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} | {'precision': 0.5454545454545454, 'recall': 0.5454545454545454, 'f1': 0.5454545454545454, 'number': 11} | {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} | {'precision': 0.75, 'recall': 0.8181818181818182, 'f1': 0.7826086956521738, 'number': 11} | 0.7903 | 0.7424 | 0.7656 | 0.9666 | |
|
| 0.1315 | 12.5 | 100 | 0.0312 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | 0.9701 | 0.9848 | 0.9774 | 0.9970 | |
|
| 0.0239 | 18.75 | 150 | 0.0371 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 | |
|
| 0.0098 | 25.0 | 200 | 0.0450 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 | |
|
| 0.0085 | 31.25 | 250 | 0.0360 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 | |
|
| 0.0065 | 37.5 | 300 | 0.0219 | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | 0.9846 | 0.9697 | 0.9771 | 0.9939 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.32.1 |
|
- Pytorch 2.2.0+cpu |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.2 |
|
|
